Embodiments of the invention generally to magnetic resonance (MR) imaging and, more particularly, MR imaging near metallic implants.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
The use of MR imaging in musculoskeletal (MSK) diagnostics is a rapidly growing field. Arthroplasty is the surgical placement of implants. The population of patients having some form of arthroplastic implant, and particularly metallic implants, is quite large and growing rapidly. However, most materials that are robust and durable enough to utilize for bone replacements will have magnetic properties that, when placed in a typical B0 magnetic field, induce extraneous fields of amplitude and spatial variation that are large compared to the field offsets utilized in conventional spatial encoding. Accordingly, these materials can introduce distortions in the main magnetic field resulting in an inhomogeneous magnetic field.
While the signal loss induced by these field gradients can largely be regained through the use of Hahn spin-echoes, the distortion they produce in both the readout and slice directions are drastic and are typically unacceptable for clinical evaluation. Despite these challenges, MRI has been shown to be quite useful in the diagnosis of degenerative conditions in arthroscopic patients. In particular, MRI has been used to screen perioprosthetic soft tissues, diagnose osteolysis, and visualize implant interfaces. These diagnostic mechanisms benefit significantly from visual information near implant interfaces. Unfortunately, artifacts induced by the implants in conventional MRI images are most severe near the implant interfaces.
A more promising approach to magnetic resonance imaging near metallic implants is known as Multi-Acquisition Variable-Resonance Image Combination (MAVRIC). MAVRIC involves the acquisition of multiple 3D MR data sets, where the center transmission frequency and the center reception frequency of each 3D MR data acquisition are set to an offset frequency that is distinct for each 3D MR data set, thus imaging different regions around a metal implant. A single image is then constructed from the 3D MR data sets (using, e.g., a sum of squares computation), the single image having reduced artifacts and reduced image distortion. However, a drawback of the MAVRIC technique is its prolonged scan time, which can reach upwards of 20-25 minutes under common circumstances.
To address the issue of prolonged scan time, it is possible to apply parallel imaging techniques to MAVRIC to reduce the overall scan time. As is known in the art, parallel imaging reconstruction can generally be divided into two categories: 1) SENSE-based techniques (Sensitivity Encoding), which estimate coil sensitivity profiles from low-resolution calibration images, which can then be used to unwrap aliased pixels in image space using a direct inversion algorithm; and 2) GRAPPA-based techniques (Generalized Auto-calibrating Partially Parallel Acquisition), which calculate reconstruction weights necessary to synthesize unacquired data directly from acquired data in k-space using an algorithm that does not require coil sensitivity estimates. However, SENSE-based parallel imaging techniques have proven to be ineffective in MAVRIC imaging, as metallic implants can cause large signal voids in images. These large signal voids create difficulties in sensitivity map estimation for SENSE-based techniques, which may result in artifacts in the final image. On the other hand, data-driven parallel imaging techniques such as GRAPPA and ARC (Autocalibrating Reconstruction for Cartesian imaging) have been shown to be insensitive to the effects of the large signal voids caused by metallic implants, as the unaliased weights in GRAPPA and ARC methods are estimated by minimizing residuals weighted by magnetization.
While data-driven parallel imaging techniques have been shown to be effective in reducing scan times for imaging around metallic implants, auto-calibrated data-driven parallel imaging techniques (such as auto-calibrated ARC) are still computationally time consuming, as calibration data is determined for an acquired 3D MR data set at each offset frequency.
It would therefore be desirable to have a system and method capable of parallel imaging for such application with reduced calibration computation time.